sinansaka

6 ms·

1.
by sinansaka 19d ago
LLMs learned to speak by reading novels which have to use many adjectives and impactful vocab to help set the scene without visual support. So no wonder we ended up with this quirk showing up. I wonder if the researchers in the lab thought
2.
by sinansaka 4mo ago
Code reviews dont scale, inherently bottlenecked by how fast you can read code + cognitive capacity. But there are ways to make it easy to make sure PRs dont break prod and keep the quality stable: linters with best practices + company spec
3.
by sinansaka 4mo ago
Is there a public test to check the performance of various captchas vs yours to verify the claims? Ps. In mobile its possible to accidentally zoom out of the website, width is not limited to screen size.
4.
by sinansaka 4mo ago
This is pretty cool, didnt even know of half the options under well-known urls. Thanks!
5.
by sinansaka 4mo ago
I was about to write what advantage it has over linters but then saw the built on section. Good work. We use megalinter with our flavour of go and vite rules, plus extensive e2e testing after each agent run. Quality of the spec driven agent
6.
by sinansaka 4mo ago
Love it! I think the minimal approach you took is the right path forward. As others mentioned, small harnesses make it possible to run many agents in parallel and in small cloud instances. working on a minimal agent in Go myself for this us
7.
by sinansaka 4mo ago
I'm betting my startup on it. The subsidised model subscription will start to dry out and providers will lean heavier into locking down how they want their models to be used (Anhropic has been paving the way already). The only way forw
8.
by sinansaka 5mo ago
S3 lifecycle policies and scheduled RDBMS jobs are the low hanging fruit here. I used to work at a data platform team and built a cleaning service that used tags and object hierarchy trees to find and clean old PII data. Not an easy thing t
9.
by sinansaka 5mo ago
nice work! I built a similar system at my previous company. It was built on top of github. agent was triggered by the created issue, run in actions, save state in PR as hidden markdown. It worked great but time to first token was slow and m